Rule Of Reason Research Articles

Formalism in Competition Law

Abstract This article analyzes the meaning and role of formalism in competition law. Drawing on general legal theory and philosophy, this article conceives of formalism as decision-making constrained by rules, whereby rules exclude considerations from the decision-making process. It analyzes the degree to which per se rules and the rule of reason in U.S. antitrust law and the category of “by object” restrictions in EU competition law involve formalistic reasoning. It subsequently discusses the relationship between “legal form” and “anticompetitive effects” and the debate on “form-based” versus “effects-based” approaches to competition law. It concludes that “effects-based” approaches to competition law typically involve formalistic legal rules, thus deconstructing the well-known form–effect dichotomy. Finally, this article analyzes the normative relationship between formalism, type 1 and 2 errors, and legal certainty, and argues that this relationship is fundamentally shaped by beliefs about institutional competence and the allocation of decisional jurisdiction. The article concludes by arguing against pejorative conceptions of “formalistic” and “form-based” competition law. Competition law, like law in general, is inherently formalistic, albeit to a limited degree. Rather than the empty dichotomy of “form” versus “effect,” the central question in competition law is to which formalism it ought to be committed.

Combining Representation Learning and Logical Rule Reasoning for Knowledge Graph Inference

Combining Representation Learning and Logical Rule Reasoning for Knowledge Graph Inference

A Satisficing Theory of Epistemic Justification

AbstractThere is now a significant body of literature on consequentialist ethics that propose satisficing instead of maximizing accounts. Even though epistemology recently witnessed a widespread discussion of teleological and consequentialist theories, a satisficing account is surprisingly not developed yet. The aim of this paper is to do just that. The rough idea is that epistemic rules are justified if and only if they satisfice the epistemic good, i.e., reach some threshold of epistemic value (which varies with practical context), and believing is justified if and only if it follows said rules.I argue that this alternative to the implicitly established way of thinking in maximizing terms has significant advantages. First, maximizing epistemic value can be unreasonably demanding; second, a satisficing theory can make finding reasonable rules for belief formation and sustenance much more accessible; and third, a satisficing approach is a better alternative to both general subjectivist and maximizing objectivist attempts to spell out epistemic blame.

Evidence fusion-based alarm system design considering coarse and fine changes of process variable

In view of the coarse and fine changes of process variable in industrial systems, this paper introduces a univariate alarm design method based on dynamic evidence fusion. Firstly, in order to describe the coarse statistical characteristics of historical sample data, the multi-transition data segmentation based on memory and forgetting strategies and the referential evidential matrix (REM) construction are presented. Secondly, the real-time sample of process variable is transformed into alarm evidence by matching with REM, and then such multiple pieces of alarm evidence continuously acquired in time are fused by evidence reasoning (ER) rule with the interval-valued fusion weights and reliabilities of alarm evidence, so as to accurately adapt the fine change of process variable. Finally, numerical experiment and motor rotor alarm experiment are implemented to validate that the proposed method has better performances than traditional alarm design methods.

How am I supposed to feel? Female students’ emotional reasoning about academic becoming in transnational higher education

This article centres on female students’ reasoning about their emotional (re)actions during the process of academic becoming. It builds on an ethnographical study of students’ subjectivity processes at a jointly run Sino-Danish university in Beijing. The article draws on a theoretical framework called emotional reasoning, bridging Sara Ahmed’s notion of emotionality and Thomas Popkewitz’ rules of reasoning, to investigate the affective structuring of students’ reasoning about academic identities in transnational education. The study elucidates how students’ reasoning about their opportunities for academic transformation is connected to racialising hierarchies of gendered and aged emotional characteristics. These interlockings can be read as reflections of the unequal interlocking of power relations in a transnational educational space. The study illustrates that, within this space, the students gain differentiated affective opportunities to act, depending on whether their bodies are surfaced as white-young-female or Chinese-young-female.

An Evidential Reasoning Rule Considering Parameter Uncertainty

This article aims to propose an evidential reasoning (ER) rule considering the parameter uncertainty. As the essential parameters, the evidence weight and reliability make the ER rule constitute a generalized reasoning framework. Theoretically, the weight is affected by subjective cognition, while the reliability mainly reflects objective variation. However, most of the recent researches have focused on the quantitative calculation methods, which make the differences in the property of the two parameters ignored. In this article, a relatively different idea from previous studies is provided, in which multisource uncertainty of parameters is fully considered. On the one hand, the weight is profiled by interval variable with lower and upper bounds. On the other hand, the reliability is modeled by random variable with probability distribution function. Then, a unified inference model for evidence aggregation is developed based on the inference process of the ER rule. In addition, some basic properties of the model are clearly presented to illustrate the rationality of parameter uncertainty. Finally, a practical example is given to show the potential applications of the proposed model.

Health Assessment of Complex System Based on Evidential Reasoning Rule with Transformation Matrix

In current research of complex system health assessment with evidential reasoning (ER) rule, the relationship between the indicators reference grades and pre-defined assessment result grades is regarded as a one to one correspondence. However, in engineering practice, this strict mapping relationship is difficult to meet, and it may degrease the accuracy of the assessment. Therefore, a new ER rule-based health assessment model for a complex system with a transformation matrix is adopted. First, on the basis of the rule-based transformation technique, expert knowledge is embedded on the transformation matrix to solve the inconsistent problems between the input and the output, which keeps completeness and consistency of information transformation. Second, a complete health assessment model is established via the calculation and optimization of the model parameters. Finally, the effectiveness of the proposed model can be validated in contrast with other methods.

Parenting Styles in the New Normal of Carrascal National High School, Division of Surigao del Sur, Philippines

The study presented in this paper sought to find the parenting styles in the new normal of Carrascal National High School, Division of Surigao del Sur, as well as the relationship between parents’ profile and their parenting style. Parenting styles being considered are: Authoritative, Authoritarian, Uninvolved, and Permissive based on the theory of Baumrind (1971). Out of 1,389 parents for School Year 2020-20201, with 95% confidence level, parents were randomly selected as part of the sample. A survey utilizing validated items was conducted to gather data from the identified respondents. Findings were based on the results from the questionnaire given to the 311 respondents. The study shows that the dominant parenting style of the parents of Carrascal National High School, Division of Surigao del Sur in the new normal is Authoritative. Findings suggest that in order to enhance the parental support to the learners of Carrascal National High School, Division of Surigao del Sur, there is a need to conduct orientation of the parents on Responsible Parenthood. The orientation must focus on the improvement of the following indicators: Encouraging children to talk about their troubles, Emphasizing the reasons for rules, Giving praise to the performance of children, Avoidance on scolding children when behavior doesn’t meet parent’s expectations, Avoidance on stating punishments to children but do not actually do them, and Not spoiling children.

Intelligent identification for vertical track irregularity based on multi-level evidential reasoning rule model

Intelligent identification for vertical track irregularity based on multi-level evidential reasoning rule model

Data classification based on attribute vectorization and evidence fusion

Classifiers based on evidential reasoning (ER) rule can well handle the uncertainty in the mapping relationship between input attributes and output classes. To avoid the number of model parameters increasing with the growing number of input attributes, this paper proposes a classification model based on attribute vectorization and evidential reasoning (AV-ER). Firstly, different input attributes are combined into attribute vectors by using principal component analysis (PCA). Then, all training samples are casted into reference attribute vectors , and the reference evidence matrix (REM) is generated by likelihood function normalization. After that, all pieces of activated evidence are fused through ER theory to generate the final classification decision. In the fusion process, parameters of the initial classification model are optimized by genetic algorithm (GA), and Akaike information criterion (AIC) is used to evaluate the model performance comprehensively considering the model complexity and classification accuracy. Finally, typical UCI benchmark datasets are applied to verify the proposed AV-ER classification model, and the results indicate that the classification performance of the AV-ER model is satisfying while the number of the model parameters decrease obviously as well.

Glance of Research Methodology for Researchers: A Logical Assessment

Research is one of the means by which we seek to discover the truth. It is based upon the tacit assumption that the world is a cosmos whose happenings have causes and are controlled by forces and relationships that can be expressed as laws and principles. Discovery of these controls of nature provides us with a hunting license to search for ways of controlling our environment. To search for truth in a scientific way research methodology provides principles to refine our common beliefs through research activity that establishes rules of logical and appropriate reasoning. In order to apply the scientific research methodology properly in research work, the researcher must have a clear basic concept of research methodology & methods that will ensure to find potential research results. This paper deals with the conceptuality of the research methodology like the meaning of the research, objectives of research, motivation in research and types of research. The basic approaches to research, research methods versus methodology, research process i.e. formulation of the research problem, extensive literature survey, development of working hypotheses, preparing the research design, execution of the project, analysis of the data, hypothesis-testing, generalizations and interpretations and preparation of the report have also been described along with the criteria of good research and problems encountered by researchers in India.

Evaluative Concepts of the European Convention on Human Rights: Current Issues of Application as Exemplified by Article 6

The article is devoted to the study of the correct application of the evaluative concepts contained in the norms of the Convention for the Protection of Human Rights and Fundamental Freedoms. Attention is drawn to certain difficulties in the application of the evaluative norms in the Convention: despite their objective necessity and usefulness, they create a room for the law enforcement entity’s own discretion up to subjectivism in resolving specific cases; they serve as an objective obstacle to the unification of the case law of the ECtHR and this creates preconditions for unequal implementation of ECtHR judgments in the national legal order; they cause the risk of errors in the application of the provisions of the Convention by the competent authorities of the Contracting States. It is established that the application of legal norms containing evaluative concepts is challenging not only owing to the specific properties of the evaluative concepts themselves, but also due to the peculiarities of adjudication by the ECtHR when applying the evaluative norms. The relevant case law of the ECtHR as well as examples of national legislation are analysed in support of these theoretical conclusions. Since the lack of uniform application of the evaluative norms of the Convention causes divergent use of the ECtHR’s judgments in the domestic judicial system, it is advised to follow a number of rules for reasoning of decisions by the domestic court. These rules will serve as a certain guarantee against ambiguous implementation of ECtHR judgments at the national level.

An Ensemble Learning Method Based on an Evidential Reasoning Rule considering Combination Weighting.

As an extension of Dempster–Shafer (D-S) theory, the evidential reasoning (ER) rule can be used as a combination strategy in ensemble learning to deeply mine classifier information through decision-making reasoning. The weight of evidence is an important parameter in the ER rule, which has a significant effect on the result of ensemble learning. However, current research results on the weight of evidence are not ideal, leveraging expert knowledge to assign weights leads to the excessive subjectivity, and using sample statistical methods to assign weights relies too heavily on the samples, so the determined weights sometimes differ greatly from the actual importance of the attributes. Therefore, to solve the problem of excessive subjectivity and objectivity of the weights of evidence, and further improve the accuracy of ensemble learning based on the ER rule, we propose a novel combination weighting method to determine the weight of evidence. The combined weights are calculated by leveraging our proposed method to combine subjective and objective weights of evidence. The regularization of these weights is studied. Then, the evidential reasoning rule is used to integrate different classifiers. Five case studies of image classification datasets have been conducted to demonstrate the effectiveness of the combination weighting method.

An extended evidential reasoning approach with confidence interval belief structure

In Dempster-Shafer theory, belief structure plays a key role, which provides a useful framework for information representation of uncertain variables. Basic Probability Assignment (BPA) is the most important component, which is difficult to be determined due to the uncertainty of information. Generally, there are two ways to get BPA of evidential theory: One is a subjective judgment of the expert’s experience, Interval Belief Structure (IBS) can solve the fuzziness and uncertainty of expert’s judgment. The other is an objective calculation by sampling existing data, in which BPA is viewed as the point estimate. Therefore, one of the contributions of this paper is that the definitions and theories of Confidential Interval Belief Structure (CIBS) is developed to describe BPA in Dempster-Shafer theory, which can give a range of population parameter values and contain more information to deal with the uncertainty and fuzziness of existing data. And then, based on evidential reasoning rule for counter-intuitive behavior, another contribution of this paper is that the extended evidential reasoning approach with CIBS is proposed to obtain the combined belief degree. The proposed method can be flexibly adjusted by appropriate errors and confidence levels, which is the main advantage. Finally, a case of sustainable operation of Shanghai rail transit system to verify the feasibility of proposed method and great performance of the extended method is shown.

Parental support perceived in the parent-child dyad

It has become a certainty that effective parenting requires a high level of control and monitoring. The parental support provided during the pregnancy predicts a good functioning of the child, generates a socially, mentally and emotionally healthy competent child, who functions well under stress (Auerbach et al., 2011).In this study we investigate the parental support perceived in the parent - child dyad (by including both parents and adolescents rather than relying only on parent reports). The participants were represented by the parent-child couple (adolescent from high schools), so the participants were 86 dyad parent child who completed the questionnaire in google form, Perceived Parental Autonomy Support Scale (P-PASS; Mageau et al. 2015). The applied questionnaire assesses parents 'perceptions of supporting their own autonomy (offering choice, recognizing the child's feelings and providing a reason for rules and requirements), parental control (criticisms of guilt, use of threats and performance pressures) and adolescents' perceptions of the same variables.Results showed that parents' perceived autonomy support from their own parents that is not associated with adolescents' perception of the liberty to make their own choices and providing rationale for rules and demands; the only supportive parenting construct reflected in adolescents' perception being the acknowledgment of their feelings.

Intelligent Reasoning Rules for Home Energy Management (IRRHEM): Algeria Case Study

Algeria is characterized by extreme cold in winter and high heat and humidity in summer. This leads to an increase in the use of electrical appliances, which has a negative impact on electrical energy consumption and its high costs, especially with the high price of electricity in Algeria. In this context, artificial intelligence can help to regulate the daily consumption of electricity, by optimizing the exploitation of natural resources and alerting the individual to avoid energy wasting. This paper proposes a decision-making tool (IRRHEM) for managing electrical energy at smart home. The IRRHEM solution is based on three elements: the use of natural resources, the notification of the inhabitants in case of resources misuse or wasting behavior, and the aggregation of similar activities at same time. Additionally, based on the proposed intelligent reasoning rules, residents’ behavior and activities are represented by OWL (Ontology Web Language) and written and executed through SWRL (Semantic Web Rule Language). Finally, the (IRRHEM) solution is tested in a home located in Algiers city inhabited by a family of four persons. The IRRHEM performance evaluation results are very promising and show a 3.60% rate of energy saving.

A generalized model of three-way decision with ranking and reference tuple

The theory of three-way decision was originally introduced as three regions to explain rough sets. Nowadays we can apply this theory to many important fields by defining reasonable trisecting rules. In this paper, we reconsider the two-universe model of three-way decision proposed by Xu et al. and extend it to a more general level. Specifically, we introduce a pair of thresholds to the model and propose a generalized model of three-way decision with ranking and reference tuple. We prove that, although the pairs of thresholds are infinite, we only need to consider a finite number of pairs of thresholds and their corresponding trisections. Based on this theoretical foundation, we further define a unique measure to assess the trisections, compare trisections through this measure, and propose an algorithm to compute the optimal trisection in finite steps. By comparison with other models and an example of application, we demonstrate that the generalized model is more expressive and more practical than the previous one, and has some advantages over the compared models in accuracy of trisecting and validity of explaining.

IoTSAX: A Dynamic Abstractive Entity Summarization Approach With Approximation and Embedding-Based Reasoning Rules in Publish/Subscribe Systems

Users are interested in entity information and may use paradigms like publish/subscribe systems in Internet of Things (IoT), where entity-centric data comes from multiple sources. The IoT application demands contributed to the data integration and semantic interoperability need while maintaining high usability and low processing system resources usage. Existing approaches dealt with semantic interoperability by using/extending ontologies with strict schemata/semantics and difficult to be updated. Other approaches tackled high usability by providing sensor data abstractions that infer high-level knowledge, but they are either complex for real-time processing or have semantic/syntactic coupling making them nonflexible. Therefore, our key question is: can a dynamic entity summarization system that provides high-level abstractions while ensuring semantic/syntactic decoupling, and low use of processing system resources, be defined? In this article, we address this question by proposing the abstractive publish/subscribe summarization system that provides abstractive summaries of numerical IoT data for user-friendly subscriptions by using IoTSAX, an enhanced symbolic aggregate approximation (SAX) methodology for dynamic IoT environments, and approximate rule-based reasoning by using embedding models. Our results for two use cases, medicine and smart cities, show that although abstraction can be 2 to 3 times slower in latency, it achieves up to 98% reduction in the notifications’ number. IoTSAX outperforms SAX in approximation error up to 2 to 3 times more and in compression space-saving percentage when data redundancy occurs, while it has a similar or better latency and throughput performance. Finally, concept hierarchy-based embedding models can achieve F-scores of up to 0.87 for approximate rule-based reasoning.

An integration method to improve the quality of global land cover

Land cover plays a major role in global climate change and biogeochemical cycles. It is widely used in ecological and Earth-system models to simulate atmosphere-biosphere exchanges of energy, water, and carbon. However, the current global land cover (GLC) products lack data consistencies in land cover classes, spatial scale, temporal extent, and the image source and have low accuracy. This study aimed to create a time-series GLC dataset with high accuracy by integrating the current multiple GLC products and correcting the spatial and temporal errors that did not conform to reasonable rules. A logistic regression indicator method was proposed to integrate CCI2012, MODIS2012, and GLCNMO2013 for spatial optimization and integration. As a result, the GLC2012 was generated with the user’s accuracy of 74.4%, which was 3.4–10.9% higher than that of the above products. The GLC2012 product was sequentially superimposed by the yearly changes of the CCI-LC product to form a time-series dataset from 1999 to 2018. Then, an abnormal temporal change detection and reconstruction model (CDR) was developed to detect and correct data sequences. The pixels with any changes from 1999 to 2018 were divided into three types of real, possible, and pseudo change. The possible change was required to determine fault or truth and pseudo change was defined as a false change. The spectral similarity method was proposed to correct the false change class. Finally, a 2000–2017 improved GLC annual time-series dataset (IGLC) with a spatial resolution of 0.01° was produced. Hence, the production of a set of high-precision, time-continuous GLC datasets is of great significance for monitoring global ecological environmental protection and urban development, and for proposing corresponding policies and recommendations.

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.